Following investment from Lloyds Banking Group and Nationwide last year, UK AI fintech, Aveni has announced the launch of its domain-specific large language model (LLM) purpose-built for the UK financial services sector, FinLLM.
Designed to meet the industry’s requirements for compliance, safety and performance, FinLLM was created by a specialist team at Aveni Labs. Aligning with FCA guidance and the EU AI Act, FinLLM integrates structured and unstructured data, adheres to ethical AI principles, and is governed by rigorous oversight — all tailored to meet the demands of one of the world’s most regulated and risk-sensitive industries.
The creation of FinLLM has been a strategic priority for Aveni. With the support of Lloyds Banking Group and Nationwide, Aveni was able to ensure that the model capabilities are grounded in real-world applications and industry needs. Their input has influenced the technical build and Aveni’s approach to governance and risk.
Joseph Twigg, CEO of Aveni, commented: “In an era where AI sovereignty is becoming increasingly important, FinLLM is a fantastic example of UK AI Innovation. FinLLM combines the brilliant minds in Aveni Labs, one of the strongest AI Labs in the country, with the AI teams at two of the UK’s leading financial services institutions – Lloyds Banking Group and Nationwide.
“The output is a highly performant model, that over coming months will start to deliver automation for a range of use cases in UK Financial Services. With data transparency, AI Safety and Ethics at its heart, FInLLM is also poised to play a key role in the adoption of AI Agents in the UK market, presenting an attractive alternative to large US- based LLMs.
“Aveni is very proud to be able to attract and retain the immense talent capable of delivering highly performant LLMs here in the UK. With the pace and scale of adoption of Generative Al, it’s essential that in the UK we retain capability to compete across all components of the value chain.”
FinLLM in testing
Extensive benchmarking shows that FinLLM consistently outperforms general-purpose LLMs on financial tasks, while maintaining performance on standard benchmark tasks. Its strong results across continual pre-training (CPT) and instruction fine-tuning (SFT) stages reflects the strength of Aveni’s data strategy, training process, and evaluation framework — delivering a model that’s ethical, compliant, and finely tuned for the needs of the financial services industry.
Ranil Boteju, group chief data and analytics officer at Lloyds Banking Group, said: “Aveni’s FinLLM will be a game changer for UK financial services. Recognising its potential, Lloyds Banking Group invested in Aveni in 2024, and since then have worked closely with Aveni to co-create FinLLM and test it on our live AI use cases.
“Having seen the FinLLM roadmap and integration with the broader Aveni product suite, I’ve been blown away with the progress and ambition. I am excited to see the transformative impact the Aveni FinLLM will have when deployed at scale across Lloyds Banking Group and the industry.”
Sri Kanisapakkam, chief data officer at Nationwide, said: “Since investing in Aveni and working closely together on co-creating the FinLLM, we are delighted to see its first iteration being released. We’re excited by the performance of FinLLM and the potential benefits it will bring both Nationwide and our customers, as we continually look to deliver better service and experience through the responsible adoption of new technologies.”
FinLLM in action
With its core capabilities now established, FinLLM is set to move from research into real-world deployment. The next phase will focus on scaling its impact across the UK financial services sector by embedding it within Aveni’s flagship products — Aveni Detect and Aveni Assist — as part of a broader shift toward agentic, domain-specialised AI.
This phase will be driven by a strategic roadmap that includes expanding the FinLLM model suite, integrating proprietary financial datasets, and aligning its capabilities with sector-specific compliance and performance demands. Key initiatives such as synthetic data generation and domain-tuned alignment strategies will ensure FinLLM delivers resilient, high-impact performance in production environments.
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